The real business risks of ignoring diversity, equity, and inclusion in your AI strategy

Over the past few months, racially motivated violence across America has reawakened the world to the ongoing need to pursue racial justice in every arena. The technology industry especially has historically been slow in efforts to promote equality in the workforce — and the danger there is multiplied by the rise in machine learning (ML) and artificial intelligence (AI), arguably the most powerful, wide-reaching enterprise technologies today.

Evolve, presented by VentureBeat, is a 90-minute event that will explore the industry-shaking issues of bias, racism, and the lack of diversity across the industry. What happens when decision-makers in tech companies simply don’t reflect the diversity of the general population? How does that directly impact how AI/ML products are conceived, developed, and implemented?

“As a Black person, I’ve experienced the harsh realities of what happens when we neglect to do the work of building creative, inclusive, diverse technologies and environments, says Evolve speaker Rashida Hodge, VP, North America Go-to-Market | Global Markets at IBM. “Technology serves as a mirror of our society. It will reveal our bias. It will reveal our discrimination. It will reveal our racism.”

The issues of diversity and inclusion, racism, and bias are coming to the surface now as AI becomes entrenched in business processes. Building and implementing these technologies is a very collaborative process; from gathering the requirements to understanding the user base, to understanding how the data should be manifested. It’s a business-transforming process that has multiple tentacles. Those involved in the process are fundamentally shaping the technologies that we interface with on a regular basis — and too often, those people are impervious to the systemic effects of non-diverse and non-inclusive environments.

Technology is now much more about the data, not just ones and zeroes, Hodge says. It’s about the process of building it, the process of running it, and how it interacts and manifests within the environment it’s built for. Too often data used in AI decision-making can reflect prejudice and bias that can perpetuate inequality for years to come.

“The challenge we have is, when you have technologies that are being built by people in a biased, non-diverse, non-inclusive environment, what are they going to do?” Hodge says. “They’re going to build technologies that mimic the behaviors and outcomes which are most familiar to their personal environments. Ultimately, you run the risk of going to market with a product that fails to reflect the reality of our society.”

Take the number of publicly available stories about biased AI technologies that have incorrectly identified people of color, for instance. Most of the time, the quick fix has been to focus on the underlying algorithm, Hodge explains.

However, the underlying bias of the people developing the technology remains in place, because the problem is really about recruiting, retaining, and ensuring the people building these technologies have diverse perspectives, dimensions, and backgrounds.

“If we continue to have these monolithic environments, we’re going to continue to build monolithic technology which does not mirror our society, and does not advance the collective of our society across all diversity dimensions,” she says.

“There’s a real business risk to ignoring the importance of diversity, equity, and inclusion,” Hodge adds. “It immediately compromises a business’s total market share, for one. As you build products, and usher in new technology, you want to embrace and drive adoption across a large customer base. They need to understand your product, your message and have an affinity for it. If only a small group feel they have this need or requirement to adopt, you’re losing out on significant market share for your business.”

The second risk is in attracting talent. One of the best ways to attract talent that can help build and advance your technology is to ensure users — which can become your future hires — are leveraging your technology. You want your talent to have an affinity for your product, the company, and create the ability to build a lasting relationship with your company. Now more than ever, given accessibility and consumability, companies are interacting daily with potential users, employers, and employees. Every interaction matters and makes a difference.

“Finally, it’s fundamentally important to understand that AI is not magic,” Hodge says. These are learning systems that require data, training, and integration. Just as we would train a young engineer to become an expert, we are training AI systems to become expert learning systems through the data, process, and people.

“At the end, AI technologies are all trained and reinforced in the same way — by data, facts, information, and by people,” she says. “We have to make sure the data is varied, and the people training and reinforcing the information are varied and diverse.

To dig in and learn how companies are addressing these issues, join us at Evolve, a 90-minute event focused on best practices on how to ensure racial fairness and equity while building products, teams, and companies with AI, ML, and large data.

The 90-minute Evolve event is divided into two distinct sessions:

  1. The Why, How & What of DE&I in AI
  2. From ‘Say’ to ‘Do’: Unpacking real world case studies & how to overcome real world issues of achieving DE&I in AI

Register for free right here.

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